It’s hard to avoid. Almost every CEO’s conversation about how IT is driving innovation inevitably comes back to the potential of big data. But data is inherently dumb. It doesn’t actually do anything unless you know how to use it. And big data is even harder to monetize due to the sheer complexity of it.

Data alone is not going to be the catalyst for the next wave of IT-driven innovation. The next digital gold rush will be focused on how you do something with data, not just what you do with it. This is the promise of the algorithm economy.

Data is the oil of the 21st century

Data is the oil of the 21st century. But oil is just useless thick goop until you refine it into fuel. And it’s this fuel – proprietary algorithms that solve specific problems that translate into actions – that will be the secret sauce of successful organizations in the future.

Algorithms are already all around us. Consider the driver-less car. Google’s proprietary algorithm is the connective tissue that combines the software, data, sensors and physical asset together into a true leap forward in transportation. Consider high frequency trading. It’s a trader’s unique algorithm that drives each decision that generates higher return than their competitors, not the data that it accesses. And while we’re talking about Google, what makes it one of the most valuable brands in the world? It isn’t data; it’s their most closely guarded secret, their algorithms.

A brave new world of opportunities

Where does this ultimately lead? Software that thinks. Software that does. Cognitive software that drives autonomous machine-to-machine interactions. Dare I say artificial intelligence? I dare. I did.

A little closer to the present day, the opportunities for organizations and technology providers alike are enormous.

For organizations, the opportunity will at first center on monetizing their proprietary algorithms by offering licensing to other non-competing organizations. Think about a supply chain company licensing just-in-time logistics algorithms to a refrigerator manufacturer that seeks to partner with a grocery chain to automatically replenish food based on your eating habits. Why invent or slowly develop sophisticated algorithms at huge cost when you can license and implement quickly at low cost?

For technology providers, a brand new opportunity exists to develop and sell algorithms that help connect their customers’ existing offerings to others via the internet of things, or a veritable ‘meshternet’ as it will become, differentiating their services in the marketplace.

This will undoubtedly become a topic of fevered questioning for CIOs at c-suite meetings once media hype increases around initiatives such as the recently announced Google Brillo, a system that allows easy connection between devices. The growth opportunities and benefits of efficiency that exist when inert things can communicate autonomously to take actions without human intervention will be something every CEO and CIO will want to explore.

The algorithm economy

This will inevitably create entirely new markets to buy and sell algorithms, generating significant incremental revenue for existing companies and spawning a whole new generation of specialist technology start-ups.

Imagine a marketplace where billions of algorithms are available, each one representing a piece of software code that solves a problem or creates a new opportunity from the exponential growth in the internet of things. As apps have revolutionized human to machine interaction, we’ll see the algorithm economy power the next great leap in machine-to-machine evolution.

Products will be defined by the sophistication of their algorithms. Organizations will be valued based not just on their big data, but the algorithms that turn that data into actions and ultimately customer impact. The bottom line is that CEOs should focus now on their proprietary algorithms, not just their big data.

Peter Sondergaard
Senior Vice President and Global Head of Research 25 years at Gartner 29 years IT Industry

Peter Sondergaard is a senior vice president in Gartner, where he is the global head of Gartner Research. Mr. Sondergaard is responsible for people management and the direction of the global research organization, which includes Semiconductors, IT Infrastructure and Operations, Communications, Software and Services Management, Business of IT, Research Operations Management, and IT provider and end-user organizational roles.

Internet of Things or not, marketing algorithms (or I would say APIs – because you would mostly likely open up the interfaces and not the core algorithm itself) is one of the key ways Big data companies can expect to monetize their intellectual assets. As you point out ‘Data itself is dumb’ and the ‘Fuel’ is the ‘refined data’. I would consider the algorithms as the ‘refinery’ and the APIs and related infrastructure (such as an API marketplace) as the ‘distribution pipeline’. IoT can feed into this refinery to add an additional dimension to Big data processing

Amusing how you imported via assumption the idea that algorithms can be patented bought and sold. That is not settled law in the US, and SCOTUS has sagely rebuked and disallowed some software patents and seems to have little patience with them in general.

In fact algorithms considered as disembodied processes or ideas or methods cannot be patented anywhere and in the EU it is settled law and you can’t patent software, software patent lawyer bs to the contrary not withstanding.

What people who think their payday depends on patenting ideas need to do is pursue a productive career path instead of taxing the economy and progress by trolling for easy money.

Neither big data nor the algorithms can act alone and ultimately impact the global economy. Both are required to work together in order to unlock the next wave of value for Corporations. This is where the true opportunity exist

Peter, interesting thoughts with which I wholeheartedly agree. I would simply argue that big data is already on that path. The modern big data platform (Hadoop, Spark, and consorts) is not as much about collecting and storing data, than it is about processing this data and extracting insight/value from it.

Stealing from Merv Adrian’s book, we could say that raw data (the oil) goes to the data lake, refined data (the fuel) is found in the data reservoir, from which algorithms (the combustion engine) draw their resources.

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